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Old 04-20-2013, 02:26 PM
Elroch Elroch is offline
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Join Date: Mar 2013
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Default Re: SVM and C-parameter selection

If I understand correctly, the fact that the selection of C is based on out of sample errors in the cross validation should imply that these problems are avoided, with high probability, if there is enough data.

The questions are what conditions are necessary to ensure this, and how can this statement can be made quantitative? Each value of C is associated with a single hypothesis through the SVM training process, but this mapping is a very complex one.

I presume the size of the data set (and hence the sizes of the training set and the out of sample sets in the cross validation) are key to robust behaviour, but how big they need to be is not so clear to me for a SVM. I suspect this particular issue may be an art rather than a science, but others surely know more.
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